Conventional image search engines rely exclusively upon the user to input into the search engine words or an image describing or depicting what the user is searching for. The results are therefore only as good as the input. Moreover, conventional image search engines put before the user a large number of images, which the user must then sift through to see if an image describing or depicting what the user was searching for is in the search results. This type of searching relies exclusively upon the human user to enter words or upload an image that already describes or depicts what the user is searching for. The search results then shift the burden to the user to sift through the results, often hundreds or even thousands of images, to see whether a relevant hit has been located. If the search results are not relevant, which the user often discovers after looking through a page or more of image thumbnails, then the user must re-enter a new search, perhaps with different words or a different starting image, and the previously downloaded images are disregarded but often remain in memory. This is an extremely inefficient way to search for and locate an image that depicts an item of interest to the user. This search consumes extensive network bandwidth by downloading to the user's device many images, requires extensive temporary memory to store the images, and consumes extensive processing resources particularly if the user needs to input multiple search terms to find an image of interest. For users who are not precisely certain what they are searching for or how to articulate precisely how to identify what they are looking for, or who do not input a machine-optimal combination of words or terminology, the search results often will not produce meaningful results. Persistent users will try again and maybe again, usually after sifting through hundreds or thousands of hits, but this comes at the expense of network, memory, and computing resources not to mention frustration to the user.
Moreover, conventional image search algorithms do not have a mechanism for “surprising” the user with an image that might be of interest to them but are not within the parameters of the originally inputted search parameters. Surprising images can be returned, but only due to linguistic homonyms or other similarities in the inputted search parameter with the image attributes located by the search engine. Some users do not know precisely what they are looking for until it is presented to their eyes, at which time they realize this image represents something of interest to them. One way to do this would be to serve up randomly selected images, but this would be pure guesswork, resulting in outlandish or bizarre outliers that have no thoughtful relationship or logical relevance to a current interest of a user. Inspiration can be visual and arrives only when the user's senses are triggered by something familiar. Conventional search engines do not allow such perspectives to emerge. In other words, conventional engines ask the user to tell the machine what the user is looking for, and the machine dutifully reports images that represent what the machine believes to be similar based on the precise search parameters inputted by the user. There is no search engine that allows the user to begin from an uncertain or ill-defined starting point and then guides that user through a journey of discovery that arrives at what the user is actually interested in by soliciting continuous feedback from the human user and thereby facilitating mutual interaction among the human and the machine.
In addition, there exist a multitude of software applications that attempt to make recommendations for users based on analyzing a user's history. A user's history can include or reflect, for example, choices the user previously made based on the user's preferences. Although a user's preferences can be constant as a whole over a long period of time, the generality of the preferences allow for a user's current specific preference or interest to be less defined. For example, a user's current preferences can be more granular or initially ill-defined compared to what can be captured by electronic recommendation systems that rely on analyzing a user's history to make a current recommendation at a specific moment in time when the user may be desiring something in particular. Thus, the granularity, subjectivity, or arbitrariness of a user's current, specific preference also allows for specific current objects or items of interest that cannot accurately be predicted based on a user's history alone. Therefore, current software applications or search engines do not help a user determine what the user's current object or item of interest is despite having access to the user's history. Moreover, although the user may know that he or she wants something, the particular object of the user's interest may be unknown even to the user until one of the user's senses is inspired or provoked. Further, thinking by the user of what the users' current object of interest is, alone, may not help the user in defining his or her current object of interest.
According to aspects of the present disclosure, a system and computer-implemented method are disclosed that guide a user to his or her current object of interest based on a sequential presentation of images one at a time representative of possible physical objects of interest.